Set Significant Letters in the Bar Plot in ggplot2

prepare data

x<-c(25.6,22.2,28.0,29.8,24.4,30.0,29.0,27.5,25.0,27.7,23.0,32.2,28.8,28.0,31.5,25.9,20.6,21.2,22.0,21.2) 
b<-data.frame(x,a=gl(5,4,20)) ## a is factor

head(b)

##      x a
## 1 25.6 1
## 2 22.2 1
## 3 28.0 1
## 4 29.8 1
## 5 24.4 2
## 6 30.0 2

anova test and Tukey multiple comparisons

m1<-aov(x~a,data=b)
TukeyHSD(m1)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = x ~ a, data = b)
## 
## $a
##       diff        lwr        upr     p adj
## 2-1  1.325  -4.718582  7.3685818 0.9584566
## 3-1  0.575  -5.468582  6.6185818 0.9981815
## 4-1  2.150  -3.893582  8.1935818 0.8046644
## 5-1 -5.150 -11.193582  0.8935818 0.1140537
## 3-2 -0.750  -6.793582  5.2935818 0.9949181
## 4-2  0.825  -5.218582  6.8685818 0.9926905
## 5-2 -6.475 -12.518582 -0.4314182 0.0330240
## 4-3  1.575  -4.468582  7.6185818 0.9251337
## 5-3 -5.725 -11.768582  0.3185818 0.0675152
## 5-4 -7.300 -13.343582 -1.2564182 0.0146983

TukeyHSD means Tukey’s ‘Honest Significant Difference’ method which create a set of confidence intervals on the differences between the means of the levels of a factor with the specified family-wise probability of coverage. From the result, only 5 and 2, 5 and 4 have significantly different.

HSD.test mutiple comparisons

library(agricolae)
results <- HSD.test(m1, "a", group=TRUE)
results$groups

##        x groups
## 4 28.550      a
## 2 27.725      a
## 3 26.975     ab
## 1 26.400     ab
## 5 21.250      b

oder.group <- results$groups[order(rownames(results$groups)),]

Using HSD.test function, we can assign our group to significant letters, the default significant level is 0.05.

Creat barplot with error bar and significant letters.

library(Rmisc)

## Loading required package: lattice

## Loading required package: plyr

## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following object is masked from 'package:purrr':
## 
##     compact

stats <- summarySE(b, measurevar="x", groupvars=c("a"),na.rm=TRUE)
stats

##   a N      x        sd        se        ci
## 1 1 4 26.400 3.2863353 1.6431677 5.2292929
## 2 2 4 27.725 2.4431878 1.2215939 3.8876570
## 3 3 4 26.975 3.9802638 1.9901319 6.3334879
## 4 4 4 28.550 2.3158872 1.1579436 3.6850933
## 5 5 4 21.250 0.5744563 0.2872281 0.9140881

library(ggplot2)
ggplot(stats, aes(x=a, y=x)) + 
  geom_bar(position=position_dodge(.5), stat="identity",
             colour="black", width = 0.5) +      # Change bar size with width = 0.5
    geom_errorbar(aes(ymin=x-se, ymax=x+se),
                  size=.5,    # Thinner lines
                  width=.2,
                  position=position_dodge(.5)) + 
  theme_classic() + geom_text(aes(x=a, y=x+se+2,label=oder.group$groups), position=position_dodge(width=0.9), size=4)#text(x = stats$a, y = stats$x+stats$se+5, labels = c("ab", "a", "ab", "a", "b"))

For the graph above, we know only 5 have significant difference with 2 and 4.